New framework for doing Targeted Learning in R inspired by the tidyverse system(link is external) of R packages.

sl3: Modern Superlearning with Pipelines

  •  A modern object-oriented re-implementation for the Super Learner algorithm, employing recently developed paradigms for R programming. 
  •  A design that leverages modern tools for fast computation, is foward-looking, and can form one of the cornerstones of the tlverse. 

tmle3: An Engine for Targeted Learning

  • A generalized framework that simplifies Targeted Learning by identifying and implementing a series of common statistical estimation procedures. 
  • A common interface and engine that accommodates current algorithmic approaches to Targeted Learning and is still flexible enough to remain the engine even as new techniques are developed. 

origami: A Generalized Framework for Cross-Validation

  • A generalized framework for flexible cross-validation. 
  • Cross-validation is a key part of ensuring error estimates are honest and preventing overfitting. It is an essential part of both the Super Learner algorithm and Targeted Learning. 

delayed: Parallelization Framework for Dependent Tasks

  • A framework for delayed computations (futures) based on task dependencies.
  •  Efficient allocation of compute resources is essential when deploying large-scale, computationally intensive algorithms.

tmle3mopttx: Optimal Treatments in tlverse

  •  Learn an optimal rule and estimate the mean outcome under the rule. 
  •  Optimal treatment is a powerful tool in precision healthcare and other settings where a one-size-fits-all treatment approach is not appropriate. 

tmle3shift: Shift Interventions in tlverse

  • Shift interventions for continuous treatments. 
  • Not all treatment variables are discrete. Being able to estimate the effects of continuous treatment represents a powerful extension of the Targeted Learning approach. 

Other Free Software

ltmle: Longitudinal Targeted Maximum Likelihood Estimation

  • Targeted Maximum Likelihood Estimation (TMLE) of treatment/censoring specific mean outcome or marginal structural model for point-treatment and longitudinal data.
  • Addresses various longitudinal interventions. 

This work by CTML Faculty and/or Staff is licensed under CC BY 4.0